Compiler controlled value prediction using branch predictor based confidence

Value prediction breaks data dependencies in a program thereby creating instruction level parallelism that can increase program performance. Hardware based value prediction techniques have been shown to increase speed, but at great cost as designs include prediction tables, selection logic, and a confidence mechanism. This paper proposes compiler-controlled value prediction optimizations that obtain good speedups while keeping hardware costs low. The branch predictor is used to estimate the confidence of the value predictor for speculated instructions. This technique obtains 4.6% speedup when completely implemented in software and 15.2% speedup when minimal hardware support (a 1 KB predictor table) is added. We also explore the use of critical path information to aid in the selection of value prediction candidates. The key result of our study is that programs with long dynamic dependence chains benefit with this technique while programs with shorter chains benefit more so from simple selection methods that favor optimization frequency. A new branch instruction that ignores innocuous value mispredictions is shown to eliminate unnecessary mispredictions when program semantics aren't violated by confidence branch mispredictions.